Determining Some Factors of the Financial Situation in the European Union Publishing Sector

Sanz José, Ángel; Bedate Ana, María; Durántez, Mariano Article Determining Some Factors of the Financial Situation in the European Union Publishing Sector Review of Economic Perspectives Provided in Cooperation with: Masaryk University, Faculty of Economics and Administration Suggested Citation: Sanz José, Ángel; Bedate Ana, María; Durántez, Mariano (2018) : Determining Some Factors of the Financial Situation in the European Union Publishing Sector, Review of Economic Perspectives, ISSN 1804-1663, De Gruyter, Warsaw, Vol. 18, Iss. 1, pp. 25-43, https://doi.org/10.2478/revecp-2018-0002 This Version is available at: https://hdl.handle.net/10419/179891 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/3.0/ REVIEW OF ECONOMIC PERSPECTIVES – NÁRODOHOSPODÁŘSKÝ OBZOR VOL. 18, ISSUE 1, 2018, pp. 25–43, DOI: 10.2478/revecp-2018-0002 © 2018 by the authors; licensee Review of Economic Perspectives / Národohospodářský obzor, Masaryk University, Faculty of Economics and Administration, Brno, Czech Republic. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 3.0 license, Attribution – Non Commercial – No Derivatives. Determining Some Factors of the Financial Situation in the European Union Publishing Sector José Ángel Sanz, 1 Ana María Bedate, 2 Mariano Durántez 3 Abstract: The publishing sector is probably a cultural industry with one of the greatest economic projections due to its huge turnover and contribution to the Gross Domestic Product of countries. In order to gain a better understanding of this sector, this paper examines a sample of the most important companies belonging to this sector in the European Union, focusing on studying their economic-financial profile. In order to achieve this, multivariate statistical techniques are used to create indicators on the evolution of the main variables and financial ratios of these companies over recent years. The specific objectives are: to summarise the information in a smaller number of factors, which in turn enable us to construct a robust and reliable synthetic indicator; and investigate the relationship between the constructed index and different variables such as company age, size and localisation. The changes experienced in this sector are reflected in the obtained results and provide a richer understanding of cultural industries. Key words: Financial Ranking, Financial Ratios, Publishing Sector, Synthetic Indicator JEL Classification: C38, G00, Z11 Received: 3 July 2017 / Accepted: 18 January 2017 / Sent for Publication: 8 March 2018 Introduction The publishing industry has been studied from several perspectives, whether for the purpose of understanding its performance and evolution or in order to predict its expected behaviour and adaptation to new technologies (IPA 2015, FEP 2015). However, economic and financial analyses of the sector have seldom been undertaken. 1 Universidad de Valladolid, Facultad de Comercio, Departamento de Economía Financiera y Contabilidad, Plaza del Campus Universitario, 1, 47011 Valladolid, Spain, e-mail: [email protected], ORCID: 0000-0001-7243-8325 2 Universidad de Valladolid, Facultad de Comercio, Departamento de Economía Financiera y Contabilidad, Plaza del Campus Universitario, 1, 47011 Valladolid, Spain, e-mail: [email protected], ORCID: 0000-0002-7554-7639 3 Universidad de Valladolid, Facultad de Comercio, Departamento de Economía Financiera y Contabilidad, Plaza del Campus Universitario, 1, 47011 Valladolid, Spain, e-mail: [email protected], ORCID: 0000-0003-3881-4010 REVIEW OF ECONOMIC PERSPECTIVES 26 With the aim of gaining a deeper understanding of specific economic sectors, their companies’ financial structure and even financial efficacy in recent years, a number of studies have been conducted that resort to financial ratios that draw upon accounting information provided by the companies themselves. The techniques of financial analysis have been applied in this way on numerous sectors of the economy, such as the banking sector (Gómez-Borrero, 1986), Spain’s electricity sector (González et al. 2013), the wine industry (Arimany et al. 2016) and several others. In the literature, we can find a fair number of studies on ratio-based business bankruptcy prediction, including those by Alfaro et al. (2008); Holsapple and Wu (2011); Lee et al. (1996); Martín-Oliver and Salas-Fumás (2012); Olson et al. (2012) and Wilson and Sharda (1994). Few contributions, however, focus on the evolution of ratios over time or in connection with the typology of companies involved. Serrano et al. (2005) use multivariate analysis techniques in order to assess the effects of company size and country on financial profitability; Costa and Carini (2016) explore differences regarding output and employment in Italian cooperative businesses by means of several analyses that factor in the geographical regions where the companies are based, while Delen et al. (2013) perform an exploratory factor analysis to identify the dimensions underlying a set of financial ratios and subsequently use decision tree algorithms to evaluate the impact of these dimensions on business results. The use of synthetic indexes is becoming increasingly common, particularly in the public sector, but also, in more recent times, in private sectors. In our country, there are specific cases where they have been employed for purposes such as the evaluation of sustainable tourism in Andalusia (Blancas et. al., 2010), the assessment of the olive sector’s sustainability in the same region (Gómez-Limón and Arriaza, 2011), or, once again, an analysis of sustainability in the tourism sector at large (Thiel and Navarro, 2013). The purpose of this paper is to build a financial performance synthetic index on the basis of profitability and solvency ratios of EU-28 companies operating in the publishing sector in years 2012 and 2014. Our paper’s original contribution lies in using composite or synthetic indexes to compare the efficiency of these companies for the above designated period and ascertain whether or not the companies’ size, age, geographical location or legal status have a bearing on their financial performance. The remaining part of this paper is structured as follows: the following section collects information on the publishing sector in Europe that provides the main data on the sector; the third section focuses on the research design by identifying the data sources, describing the study population and outlining the several stages needed to produce a synthetic index; the fourth section is devoted to presenting the paper’s results in their various aspects; finally, the paper closes with a section devoted to conclusions as well as a list of the bibliographical references used. 1. The publishing sector in Europe Cultural industries in Europe constitute a relevant economic sector: one that is even ahead of other sectors such as the energy business or agriculture. In 2010, creative industries contributed roughly 4.5% to the gross value added of the then EU-27 —more Volume 18, Issue 1, 2018 27 specifically, 6.5% in the United Kingdom, 4.9% in France, 4.2% in Germany, 3.8% in Italy and 3.6% in Spain. The percentages of those employed in cultural industries out of all employed show a correlation with the above, with a global 3.8% for the whole of EU-27 and member states percentages as follows: 5.4% in the United Kingdom, 3.7% in France, 4.1% in Germany, 3.6% in Italy and 3.5% in Spain (Tera Consultants, 2010). Furthermore, the publishing industry is the subsector that generates the greatest wealth within the entire range of cultural industries. To mention a single example, data for the United Kingdom are revealing, because in 2014, the nominal contribution of the British publishing sector to the gross value added increased by 2.8% compared with the previous year and has increased by an average of 1.6% year over year in the period 2008 – 2013 (Creative Industries Council 2014). According to data supplied by the Federation of European Publishers (FEP 2015), the total revenue obtained in 2014 by book publishers in the 28 countries making up the European Union amounted to €22 billion, while the number of full-time employees working in this industry reached 125,000. In comparison with 2012, there was a decrease both in revenue (2.22%) and in employment (3.85%), while the publication of new titles rose (1.87%), as well as the number of titles in print (active catalogue) (87.78%). Since the slowdown of 2009 has ended, the sector’s growth over the last few years has been tied to an increase in exports, the licensing of translation rights and sales of e-books. The publishing industry has a large potential thanks to opportunities afforded by technological advances and development of communications, but it also faces numerous problems, as it is forced to quickly adapt to changes in the population’s consumption patterns. According to predictions made in a report titled Global Entertainment and Media Outlook: 2013-2017 elaborate by PricewaterhouseCoopers (2013), it is expected that by 2017, digital media will represent 37% of all advertising revenue, in contrast with their share of 26% in 2012, while purchased physical books will only account for 53% of consumers’ total spending. The publishing industry is introducing innovations in its production models and combating piracy and unfair competition. Regarding the sector’s legislation, there are differences in prices and VAT rates applicable across EU countries. In most of them, however, the price of books is regulated through so-called fixed price agreements. As an example, we may mention the Loi 81/766, of 10 August 1981 in France, and Ley 10/2007, of 22 June 2007 on reading, books, and libraries in Spain, as well as Decreto-Lei 176/96, of 21 September 1996 in Portugal. These countries include Austria, Denmark, Germany, Greece, Hungary, Italy, the Netherlands, Portugal, France and Spain, which thus subscribe to the official position of the European Parliament. Fixed price laws establish that publishers are entitled to fix the price of books, which must then remain identical in all sales points, notwithstanding the possibility of applying discounts of a maximum of about 5%, the exception being book fairs and sales to public libraries. On the other hand, some countries resort to a free price system. Among them are Belgium, Finland, Ireland, Luxembourg, the United Kingdom, Sweden and Switzerland, where there is opposition to protectionist measures in the cultural sector. A controversial issue in most countries, however, is the VAT rate applicable to printed and digital books as regulated by Directive 2008/8/EC, which claims that an electronic book is a digital download service which must be taxed at rates between 18% and 21%, while printed books have reduced VAT rates in all REVIEW OF ECONOMIC PERSPECTIVES 28 countries with the exception of Denmark (25%). In 2015, France and Luxembourg changed their tax rate to 20% and 17%, respectively, following a ruling by the European Union’s Court of Justice after several years of recurring breaches of European law. An important attribute of this sector is its degree of concentration, both in geographical and business terms. According to the report The Global Ranking of the Publishing Industry (Rüdiger Wischenbart Content & Consulting, 2015), in 2014, the top 57 publishing groups accumulated revenue worth €59,328 million, i.e. 11% more than in 2013, while the 10 largest groups alone earned €31,820 million. In 2014, the most powerful European markets were Germany, the United Kingdom, France, Spain and Italy, whose total market value is estimated at €36-38 billion. On the other hand, even though numerous companies are based in these countries, it is only a small number of them that dominate the market, the remainder being occupied by a large number of small and medium-sized businesses. For example, in Germany, the big companies with total sales worth over €50 million take up nearly 70% of the sector’s total revenue; in Spain, 4% of the largest firms represent over 64% of the industry’s turnover; in the United Kingdom, over 50% of companies have annual sales figures below £100,000. In the French book distribution sector, the top five companies have a combined market share of 50%. 2. Research design After this bird’s eye view of the publishing sector in Europe, we will conduct an empirical analysis of available financial data corresponding to a selection of European companies. This section details the analysed population as well as the sources of financial data used, and describes the method for building a synthetic index using the multivariate procedure of factorial analysis. 2.1. Study population and data sources The data have been extracted from Amadeus (2016), a database compiled by Bureau Van Dijk, and therefore do not constitute a random sample of all EU-28 companies. However, they do provide valuable economic and financial information on a set of 2,356 businesses. The editorial sector contains great disparities among the companies that make it up. For this reason, and in order to work with companies that are as homogeneous as possible, we decided to include in the sample only those whose operating revenue was above €1,000,000 in the year 2014. Moreover, we singled out those companies for which we had data concerning all ratios employed in the factor analysis for the years 2012 and 2014 —i.e., the data we would need in order to define their financial performance index. Based on these criteria, we were eventually left with a total number of 794 businesses. 2.2. Constructing synthetic or composite indexes The construction of composite indexes has been used for some time now in multiple public management tasks such as those involving the economy and social development. More generally, the procedure has been employed in many instances of scientific research (Saisana and Tarantola, 2002; Munda, 2005; Nardo et al. 2008). Volume 18, Issue 1, 2018 29 Pertaining methodology, we need to construct a function containing several variables that can together measure a single characteristic in the individual items under scrutiny (Schuschny and Soto, 2009). As for the reasons that justify the use of synthetic indexes, these would have to do with “the ability to sum up and interpret information as well as to evaluate in a simple way complex and multidimensional phenomena” [our translation] (Blancas et al., 2010, p.3). The use of indexes does not only make it possible to summarise a set of data, but moreover, it enables us to observe tendencies in the situation that is being studied, analyse their evolution over time and even make future predictions. This in turn allows us to make comparisons or establish rankings, both over time (longitudinal) and at specific points in time (cross-sectional). One of the most common criticisms of this approach is that it involves subjective choices by the analyst along the several stages of the process: singling out the simple indicators to work with, normalising data or setting up a weighting system are compulsory tasks which no doubt involve prior connotations and may bias results in one direction or another. This is why it is critical to reduce such subjectivity as much as possible by employing a methodology that enables us to cut down on the number of decisions to be made by the analyst (Munda, 2005). It also becomes particularly relevant to explain in a clear and precise way the process whereby the index is constructed, thus facilitating the understanding of final results. Adhering to recommendations of several authors (Boulanger, 2009; Nardo et al., 2005; OECD, 2008), the present paper proposes the following stages in the construction of a composite index: conceptual framework, variable identification and selection, measuring, weighting, aggregating and disseminating results. In our case, the aim is to construct a synthetic index that represents the financial performance (FPI) of companies in the publishing sector of EU-28. This index will then be used to conduct a series of further studies on the similarities and differences that can be found in the sector itself. The starting point was to define a series of variables or subindicators that portray the financial situation of the companies under scrutiny. Table 1 features the variables’ code and name as well as their description, both in the case of variables used in constructing the index and also of variables that will later be used to conduct specific studies. The weight assignment procedure poses some challenges, since it is in this part of the process where a possibly biased approach may produce significant differences among the indicators thus obtained, which would in turn weaken their validity (Nardo et al. (2005). Multivariate techniques provide adequate solutions and, more particularly, principal component analysis —i.e., the method chosen for the purposes of this research— eliminates redundant information by correcting overlaps that may exist between two or more variables. On the above-mentioned initial data, we applied a factor analysis whereby factors were extracted by principal components in order to conduct the weighting procedure using percentages of explained variance. REVIEW OF ECONOMIC PERSPECTIVES 30 Table 1. Variables: codes and description Code Variables Description R1 ROE using P/L before tax (%) (Profit before tax / Shareholders funds) * 100 R2 ROA using P/L before tax (%) (Profit before tax / Total assets) * 100 R3 ROE using Net income (%) (Net income / Shareholder funds) * 100 R4 ROA using Net income (%) (Net income / Total Assets) * 100 R5 Profit margin (%) (Profit before tax / Operating revenue) * 100 R6 EBITDA margin (%) (EBITDA / Operating revenue) * 100 R7 EBIT margin (%) (EBIT / Operating revenue) * 100 R8 Cash flow / Operating revenue (%) (Cash flow / Operating revenue) * 100 S1 Current ratio Current assets / Current liabilities S2 Liquidity ratio (Current assets - Stocks) / Current liabilities S3 Solvency ratio (Asset based) (%) (Shareholders funds / Total assets) * 100 S4 Gearing (%) ((Non current liabilities + Loans) / Shareholders funds) * 100 OR Operating revenue (Turnover) th. EUR Total operating revenues (Net sales + Other operating revenues+ Stock variations). The figures do not include VAT PL P/L for period (Net Income) th. EUR Net income for the year before deduction of minority interests if any (Profit after taxation + Extraordinary and other profit). TA Total assets th. EUR Total assets (Fixed assets + Current assets) NE Number of employees (last value) Total number of employees Source: Amadeus (2016) Volume 18, Issue 1, 2018 31 In the aggregation stage, additive and multiplicative methods are the most typical —the weighted sum of indicators being the most common (Gómez-Limón and Arriaza, 2011). In our case, we have used the following formula: FPI = ∑FSi x wi j i=1 (1) Where FSi are factor scores obtained by each company in each of the components defined, while wi is the percentage of explained variance of each component. wi=Explained variance for each component ∑explained var.of the whole set of selected components j i=1 (2) Once indexes have been calculated for each company and every year, the combined information is provided in a summarised and compiled form. 3. Results We now present the results, commencing with a general descriptive analysis of the firms’ financial data and defining the interest groups so as to interpret the index. The results of the factorial analysis are then provided and used to build the synthetic index. The section concludes with an analysis of the financial performance index from various standpoints: depending on the type of firm and the quintile of the indicator. 3.1. Initial analysis In this section, we will present the sector’s aggregated data. First, a joint study is conducted, where the values of all variables are presented in a systematic, summary-like form. Second, we divide the assessed companies into several groups according to four different typologies. a) Descriptive analysis Table 2 and Table 3 show a descriptive study of the analyzed variables. A first look at the data shows that Pearson’s coefficients of variation are very high, which points to a large dispersion for all variables. By comparing average values for the two years under scrutiny, we observe that all profitability ratios for 2014 rose when compared to 2012, that is with the exception of the two ratios related to the ROE variable. Profit margin saw the highest increase (9.70%), whereas the most important drop was that of the ROE variable when estimated on the basis of net income (12.79%). As regards structural ratios, the only increase takes place in the solvency ratio, while the liquidity ratio remains stable and both the current ratio and financial gearing undergo a decrease, which is more pronounced (8.46%) in the latter case. Regarding size-related variables, profits in 2014 increased compared to 2012 (4.30%), as did total assets (2.53%), while the average values for both operating revenue and number of employees dropped. REVIEW OF ECONOMIC PERSPECTIVES 32 Table 2. Descriptive analysis 2014 N Mean Median Std. Dev. Coeff. Var. (%) Minimum Maximum R114 794 15.90 10.54 48.57 305.49 -394.43 439.24 R214 794 6.47 4.14 12.21 188.73 -43.60 82.14 R314 794 9.41 6.90 41.73 443.71 -394.43 408.73 R414 794 4.50 3.01 9.97 221.58 -46.07 56.34 R514 794 5.88 4.10 11.87 201.97 -50.85 53.52 R614 794 8.83 7.25 11.72 132.79 -46.60 53.87 R714 794 5.75 4.63 10.98 190.99 -46.94 51.06 R814 794 7.16 5.76 10.99 153.44 -46.60 52.00 S114 794 2.67 1.80 2.80 104.81 0.24 34.11 S214 794 2.03 1.30 2.35 115.69 0.03 22.36 S314 794 46.44 44.35 24.15 52.01 1.08 97.06 S414 794 60.69 17.62 107.52 177.16 0.00 814.36 OR14 794 20315.70 3622.20 90155.20 443.77 1001.09 1793000.00 PL14 794 1492.23 105.23 13670.10 916.08 -22743.60 345482.00 TA14 794 25944.70 4109.99 114885.00 442.81 155.47 2214800.00 NE14 665 93.41 22.00 470.17 503.33 1.00 8554.00 Source: Own elaboration Table 3. Descriptive analysis 2012 N Mean Median Std. Dev. Coeff. Var. (%) Minimum Maximum R112 794 16.13 11.29 52.96 328.32 -391.76 587.48 R212 794 6.03 4.27 12.30 203.90 -52.79 87.51 R312 794 10.79 7.82 43.24 400.80 -341.10 428.08 R412 794 4.33 2.95 10.18 235.13 -48.87 72.45 R512 794 5.36 3.75 12.02 224.19 -74.13 66.39 R612 794 8.31 6.85 11.93 143.47 -52.42 67.69 R712 794 5.36 4.41 11.14 207.96 -56.23 63.21 R812 794 6.79 5.45 11.19 164.79 -59.73 67.54 S112 794 2.73 1.81 3.08 112.80 0.09 30.41 S212 794 2.03 1.32 2.45 120.32 0.03 25.29 S312 794 45.80 44.69 24.63 53.77 1.35 97.73 S412 794 66.30 18.57 123.45 186.19 0.00 998.30 OR12 794 21747.80 3512.01 103767.00 477.14 289.53 1995700.00 PL12 794 1430.71 90.13 8955.46 625.95 -22075.90 128900.00 TA12 794 25305.40 3934.03 112829.00 445.87 126.17 2143500.00 NE12 637 94.68 21.00 512.00 540.77 1.00 10152.00 Source: Own elaboration Volume 18, Issue 1, 2018 39 observe an increase as we move into a higher quintile (see Table 13): in other words, the higher the index is, the larger the companies’ profits are. These differences are statistically significant, as is made evident in Table 15. Another observation that corroborates a strong positive correlation between the obtained index and the variable reflecting profits and losses is the fact that Spearman’s rank correlation coefficient is 0.7352 for the year 2014 and 0.7547 for the year 2012. Table 13. Summary Statistics for Profits-and-Losses QUIN14 N Mean 2014 Median 2014 Mean 2012 Median 2012 1 159 -750.256 -106.673 94.656 2.661 2 159 243.178 23.331 733.474 29.943 3 158 1293.060 106.263 1595.670 90.284 4 159 1393.160 270.658 1519.960 163.931 5 159 5280.770 616.476 3210.830 454.367 Total 794 1492.230 105.231 1430.710 90.134 Source: Own elaboration On the other hand, between 2012 and 2014, there was a 2.81% decrease in employment (see Table 14) in the companies making up the study sample (an occurrence that, as was mentioned in this paper’s introduction, is shared by the whole of the companies in the sector). Such a decrease, however, is not equally distributed but rather depends on the quintile a given firm’s index for 2014 occupies. In other words, companies included in the first four quintiles did experience a decrease in their number of employees, although there are differences among them, since companies in the third quintile experienced a bigger drop in this regard with staff cuts equivalent to 10.77%, in contrast with the 3% observed in the other three quintiles. However, companies with a high index increased their staff by 9.68%. The high degree of dispersion exhibited by this variable within every single quintile defined for the purpose of this study means that these differences between values in the several quintiles are not statistically significant, as is shown in Table 15. Table 14. Summary Statistics for Number of Employees QUIN14 N Mean 2014 Median 2014 Mean 2012 Median 2012 1 131 50.244 19.0 52.710 18.0 2 118 100.890 23.0 105.059 23.0 3 119 139.790 20.0 156.664 18.0 4 121 68.736 26.0 69.752 27.0 5 122 118.377 25.5 107.926 26.5 Total 611 94.732 22.0 97.466 22.0 Source: Own elaboration REVIEW OF ECONOMIC PERSPECTIVES 40 Table 15. Statistics for equality of variance and equality of means/medians contrasts. Profits-and-Losses and Number of Employees Variable Factor Levene P-value Kruskal-Wallis P-value PL14 QUIN14 8.2786 0.0000*** 432.34 0.0000*** PL12 QUIN14 4.6030 0.0011*** 159.98 0.0000*** NE14 QUIN14 1.9698 0.0976* 4.35 0.3606 NE12 QUIN14 2.2544 0.0620* 3.71 0.4472 * Significant at 10%, ** Significant at 5%, *** Significant at 1%. Source: Own elaboration Conclusions Cultural industries have consolidated their position in Europe as an outstanding economic sector, both from the point of view of gross value added and in respect of employment. Within these cultural industries, it is the publishing sector that contributes the greatest wealth, providing full-time jobs in EU-28 for 125,000 people and generating a total revenue worth €22 billion. A distinctive characteristic of this sector is its high degree of both geographic and business concentration. With the aim of studying the publishing sector, this paper has defined a synthetic index that measures the financial performance of companies in this sector. In order to construct such an index, we have singled out a number of profitability and structural ratios that collect information on the financial situation of the companies under scrutiny from several points of view. Our first analysis of the data showed that between 2012 and 2014, there was a widespread increase in profitability ratios —with the exception of ROE—, solvency, profits and total assets. Gearing, operating revenues and the number of employees experienced a decrease. In order to create the financial performance index, factor analysis boils down the information provided by the initial 12 ratios into 4 new factors that account for nearly 90% of the total variance for each of the two years targeted by our study, so that the index meets reliability standards. These new factors are: business profitability, financial or shareholder value, liquidity and solvency. The results obtained show that the majority of companies reach index scores within the central interval; about 70% of them fall into this interval. As for the analysis by business type, the constructed financial performance index shows differences regarding company age, the highest values being reached by the youngest companies. There are also significant differences in connection with the geographical regions where companies are based, with Eastern Europe (Croatia, Slovakia, Slovenia, Hungary, Poland and the Czech Republic) standing out from the rest. In terms of size, there are no significant differences between the defined categories, even though it may be observed that when the companies’ total assets rise, so does their FPI. As regards legal form, no significant differences were detected. Volume 18, Issue 1, 2018 41 Moreover, when the FPI is grouped into quintiles and its influence on other variables is analyzed, the results obtained for profits and losses show significant differences across quintiles, where the highest scoring companies achieve the best results —an occurrence that is completely consistent with the index’s meaning. When the same effect is analyzed from the point of view of employment, it turns out that the companies with the highest index are the only ones that have increased their staff numbers, while the rest have lost jobs in all cases, albeit not following a single pattern. As a final conclusion, we may argue that the main contribution of this paper is the design of a model for evaluating financial performance in companies within the publishing sector that has enabled us to measure the efficiency of each business with regard to the rest while keeping in mind its profitability and financial structure variables. The technique used to construct the synthetic indicator proves to be robust and is supported by a number of applications in other fields of research. One prominent future line of research may thus involve this type of study being replicated over time in an effort to examine the stability of the results obtained in the publishing sector. It may also be applied to firms in any other financial sector, comparing the outcomes with those that emerge here. Disclosure statement: No potential conflict of interest was reported by the authors. References ALFARO, E., GARCÍA, N., GÁMEZ, M. and ELIZONDO D. (2008). 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